Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

Trigo-González, Mauricio; Cortés-Carmona, Marcelo; Marzo, Aitor; Alonso-Montesinos, Joaquín; Martínez-Durbán, M.; López, Gabriel; Portillo, Carlos; Batlles, F.J.

doi:10.1016/j.renene.2023.01.111

Cited by 11 publications

(4 citation statements)

References 57 publications

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“…However, it is often infeasible to utilize accurate and detailed meteorological data regarding temperature and humidity conditions in the vicinity of a solar PV power plant. To overcome this limitation, researchers have sought to enhance prediction accuracy by integrating remote sensing techniques with NWP, thus considering the spatiotemporal characteristics of external parameters relevant to solar PV generation [31]- [35]. By combining multiple sources of data instead of solely relying on NWP or remote sensing data individually, researchers can simultaneously integrate spatiotemporal characteristics at various scales.…”

Section: Introductionmentioning

confidence: 99%

Solar PV Generation Prediction Based on Multisource Data Using ROI and Surrounding Area

Kim,

Suh

2024

IEEE Trans. Geosci. Remote Sensing

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This study introduces a lightweight hybrid solar PV generation prediction model operating on 1-hour intervals, utilizing remote sensing data to enhance power grid management. Multisource remote sensing data, including spatial features from infrared satellite images and temporal data from various hourly recorded datasets, capture spatiotemporal characteristics. The model defines and synthesizes regions of interest (ROI) and surrounding areas of ROI (ROIsurr) within satellite images to reduce computational load. Integration of image and numerical weather prediction (NWP) process modules ensures accurate prediction. Comparative analysis against five machine learning algorithms shows significant improvements, with up to a 33.7% decrease in mean absolute error (MAE) and a 19.51% decrease in root mean square error (RMSE). Additionally, the model consistently meets ASHRAE Guideline 14 standards and outperforms single-source data models. Experimentation highlights the effectiveness of smaller ROIs in enhancing predictive accuracy, demonstrating adaptability to climate variations. This lightweight multisource remote sensing-based hybrid model promises to guide smart grid operations and sustainable power grid systems, advancing remote sensing applications in renewable energy management.

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Section: Introductionmentioning

confidence: 99%

Solar PV Generation Prediction Based on Multisource Data Using ROI and Surrounding Area

Kim,

Suh

2024

IEEE Trans. Geosci. Remote Sensing

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“…In this context, renewable energy sources emerge as a competitive alternative to conventional energy sources. In addition to exploiting their own unlimited resources, renewable energy technologies are better adapted to said restrictive environmental requirements [1]. The International Renewable Energy Agency (IRENA) [2] reports that wind and solar were the renewable energy technologies that grew the most between 2016 and 2020.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Performance of mc-Si and CdTe Modules Under Soiling Conditions

Ferrada,

Llaguno,

Marzo

et al. 2024

SiliconPV Conf Proc

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In this work, we study the soiling impact on the performance of two PV technologies installed in the south of Spain. For this purpose, we include electrical measurements as well as spectral aspects such as dust transmittance, quantum efficiency and the local solar spectrum to analyse their response. The studied technologies were multicrystalline silicon (mc-Si) and cadmium telluride (CdTe) PV modules. The solar spectra representative for clear sky, clean glass cover and soiled glass as well as the spectral response of the PV modules were used for the calculation of the photogenerated current density and spectral mismatch factor. Two cloudless days were chosen for analysis. In terms of the solar resource, these days did not exhibit large differences. Nevertheless, soiling was present to a different extent. When comparing the corresponding power output of the technologies on day 1 and day 2, the power losses at 12 UTC were 3 times larger for mc-Si and nearly the same for CdTe. This result shows that the soiling impacts differently on PV modules having different spectral responses.

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“…Space and water heating in winter, which accounts for ~60% of the energy consumption in buildings, is a major concern for life support and thermal comfort 5 – 10 . Solar energy has long been exploited as a sustainable resource, as solar photovoltaic power accounts for 4.66% of global electricity generation 11 – 13 . For energy-efficient heating or cooling in buildings, utilizing solar thermal energy in households is an alternative option as it eliminates the need to convert solar energy into electricity before utilizing it for heating purposes 14 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Supercooled erythritol for high-performance seasonal thermal energy storage

Yang,

Shi,

Liu

et al. 2024

Nat Commun

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Seasonal storage of solar thermal energy through supercooled phase change materials (PCM) offers a promising solution for decarbonizing space and water heating in winter. Despite the high energy density and adaptability, natural PCMs often lack the necessary supercooling for stable, long-term storage. Leveraging erythritol, a sustainable mid-temperature PCM with high latent heat, we introduce a straightforward method to stabilize its supercooling by incorporating carrageenan (CG), a bio-derived food thickener. By improving the solid-liquid interfacial energy with the addition of CG the latent heat of erythritol can be effectively locked at a very low temperature. We show that the composite PCM can sustain an ultrastable supercooled state below −30 °C, which guarantees no accidental loss of the latent heat in severe cold regions on Earth. We further demonstrate that the common ultrasonication method can be used as the key to unlocking the latent heat stored in the CG-thickened erythritol, showing its great potential to serve as a high-performance, eco-friendly PCM for long-term seasonal solar energy storage.

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Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

Cited by 11 publications

References 57 publications

Solar PV Generation Prediction Based on Multisource Data Using ROI and Surrounding Area

Solar PV Generation Prediction Based on Multisource Data Using ROI and Surrounding Area

Analysis of the Performance of mc-Si and CdTe Modules Under Soiling Conditions

Supercooled erythritol for high-performance seasonal thermal energy storage

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